Feature selection and multiple classifier fusion (MCF) are effective approaches to improve land cover classification accuracy. In this study, we combined phenological metrics and the MCF method to map land cover types in Jiangsu province of China during the second crop growing season using moderate resolution imaging spectroradiometer time-series data. Eight phenological metrics were developed and calculated, and a MCF scheme was proposed by combining a simple majority vote and the measurement of posterior probabilities. The four base classifiers (i.e., the maximum likelihood classifier, the Mahalanobis distance classifier, the support vector machine classifier, and the neural networks classifier) and the MCF method were used in classifications using two spectral indices from the original satellite data (direct classification) and the computed metric data (metrics-based classification). Accuracy assessments indicated that the overall accuracies and kappa coefficients of the metrics-based classifications were all higher than those of direct classifications. The average overall accuracy and kappa coefficient of metrics-based classifications were 8.36% and 0.1 higher than that of direct classifications, respectively. Similarly, the overall accuracy and kappa coefficient of MCF generally were close to or exceeded the highest accuracy among all the base classifiers. The highest overall accuracy and kappa coefficient was achieved by classification with the MCF method based on phenological metrics (m-MCF), which were 88% and 0.85, respectively. Our results suggested that combining phenological metrics and MCF in classification is a promising method for land cover mapping in regions where strong phenological signals can be detected.
Knowledge of the area and distribution of cropland is important for land management and land security. Low spatial
resolution imagery is one of the important remote sensing data source in the study of the large extent cropland. There
exist many mixed pixels and effective method that should be improved to deal with them. In this paper, linear mixing
model was used to unmix the time series of MODIS-NDVI data. The emphasis was the identification and extraction of
endmembers, which represent the spectral characteristics of the single pure land cover types. A new endmembers
extraction algorithm based on the temporal series of MODIS-NDVI and TM sample data was presented in this paper. We
used the effective endmembers to linear spectral mixture model to achieve the wheat area in the study area. Regarding
the classification of TM as the reference data, we evaluated the classification results and found wheat distribution's
region accuracy and pixel accuracy reach to 92.9% and 0.837 respectively, which were higher than the clarification result
based on the endmembers from MODIS-NDVI pixel purity index analysis or from classifications of TM data. This shew
that our endmembers extraction algorithmwas available and effective, which helped to improve monitoring accuracy of
large scope and distribution of vegetation.
An integrated approach of ISODATA and SVR is presented to extract the objective information, e.g. wheat, which can
adequately combine the advantages of both hard and soft classification. It exploits the classification method of
ISODATA for the typical objective feature and SVR mixed spectral unmixing for the mixed objective feature. The
accuracy assessment shows that this method, which can obtain a higher accuracy than that of either linear spectral
unmixing or ISODATA method, is practical.
The basic idea of current study of crop growth monitoring is to analyze the relation between the shape variety of NDVI curve and the condition variety of crop, calculate the feature factors, and speculate the growing condition of crop. This investigation takes five high-yield provinces as study area, including Hebei, Henan, Shandong, Anhui and Jiangsu, and takes winter wheat as study object. The ten days maximum value composite (MVC) SPOT-VEGETATION dataset, from 1999 to 2005, is used as the main remotely sensed data. Savizky-Golay filter method, which made the NDVI time-series curve disclose the change rule of winter wheat growth better, is use to eliminate the noise. And then the method of Change Vector Analysis (CVA) is applied to detect the change dynamics of winter wheat. According to the each average value of Change Vector in six years, changes, intra-annual, inter-annual and interlocal, of winter wheat have been quantified. The result shows that the method of Change Vector Analysis is effective for monitoring the winter wheat growth as a new idea, which can integrate most of the feature factors of NDVI curve.